This disclosure relates generally to information analytics, and more particularly to generating information necessary for handling ambiguous joins within any information provider.
In relational databases, a join operation matches records in two tables. The two tables must be joined by at least one common field, i.e., the join field is a member of both tables. A join operation is usually part of a query. A typical OLAP-Query, for example, contains subtotal lines for many dimensions, and an overall total. Therefore, the measures contained in the query have to be aggregated on different levels. Previous methods include techniques to locally aggregate the data before the join, considering the requested aggregation level by building a partial query and then returning the data. However, it is not possible to build higher level subtotals by aggregating the measures over all dimensions contained in the result set. Also previous methods are not able to correctly handle all cases of table layouts with more than one join.
What is need is a method that allows to aggregate measures contained in data layouts involving ambiguous joins without reading the data several times on different aggregation levels, so data caches can be used.